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Items: 1 to 20 of 102

1.

Causal inference when counterfactuals depend on the proportion of all subjects exposed.

Miles CH, Petersen M, van der Laan MJ.

Biometrics. 2019 Feb 4. doi: 10.1111/biom.13034. [Epub ahead of print]

PMID:
30714118
2.

Marginal Structural Models with Counterfactual Effect Modifiers.

Zheng W, Luo Z, van der Laan MJ.

Int J Biostat. 2018 Jun 8;14(1). pii: /j/ijb.2018.14.issue-1/ijb-2018-0039/ijb-2018-0039.xml. doi: 10.1515/ijb-2018-0039.

PMID:
29883322
3.

Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population.

Sofrygin O, van der Laan MJ.

J Causal Inference. 2017 Mar;5(1). pii: 20160003. doi: 10.1515/jci-2016-0003. Epub 2016 Nov 29.

4.

Collaborative double robust targeted maximum likelihood estimation.

van der Laan MJ, Gruber S.

Int J Biostat. 2010 May 17;6(1):Article 17. doi: 10.2202/1557-4679.1181.

5.

Causal Inference for a Population of Causally Connected Units.

van der Laan MJ.

J Causal Inference. 2014 Mar;2(1):13-74.

6.
7.

Estimating the Population Average Treatment Effect in Observational Studies with Choice-Based Sampling.

Zhang Z, Hu Z, Liu C.

Int J Biostat. 2019 Apr 16. pii: /j/ijb.ahead-of-print/ijb-2018-0093/ijb-2018-0093.xml. doi: 10.1515/ijb-2018-0093. [Epub ahead of print]

PMID:
30990786
8.

A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.

Saul BC, Hudgens MG.

J Stat Softw. 2017;82. pii: 2. doi: 10.18637/jss.v082.i02. Epub 2017 Nov 29.

9.

On inverse probability-weighted estimators in the presence of interference.

Liu L, Hudgens MG, Becker-Dreps S.

Biometrika. 2016 Dec;103(4):829-842. doi: 10.1093/biomet/asw047. Epub 2016 Dec 8.

10.

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

Schuler MS, Rose S.

Am J Epidemiol. 2017 Jan 1;185(1):65-73. doi: 10.1093/aje/kww165. Epub 2016 Dec 9.

PMID:
27941068
11.

Dependent Happenings: A Recent Methodological Review.

Halloran ME, Hudgens MG.

Curr Epidemiol Rep. 2016 Dec;3(4):297-305. doi: 10.1007/s40471-016-0086-4. Epub 2016 Jul 28.

12.

Targeted maximum likelihood based causal inference: Part I.

van der Laan MJ.

Int J Biostat. 2010;6(2):Article 2.

PMID:
21969992
13.

Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function.

Zheng W, Petersen M, van der Laan MJ.

Int J Biostat. 2016 May 1;12(1):233-52. doi: 10.1515/ijb-2015-0036.

14.

Identification and Estimation Of Causal Effects from Dependent Data.

Sherman E, Shpitser I.

Adv Neural Inf Process Syst. 2018 Dec;2018:9446-9457.

15.

Toward Causal Inference With Interference.

Hudgens MG, Halloran ME.

J Am Stat Assoc. 2008 Jun;103(482):832-842.

17.

A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.

Balzer LB, Zheng W, van der Laan MJ, Petersen ML; SEARCH study.

Stat Methods Med Res. 2018 Jan 1:962280218774936. doi: 10.1177/0962280218774936. [Epub ahead of print]

PMID:
29921160
18.

A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome.

Gruber S, van der Laan MJ.

Int J Biostat. 2010;6(1):Article 26. doi: 10.2202/1557-4679.1260. Epub 2010 Aug 1.

19.

Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Bahamyirou A, Blais L, Forget A, Schnitzer ME.

Stat Methods Med Res. 2018 Jan 1:962280218772065. doi: 10.1177/0962280218772065. [Epub ahead of print]

PMID:
29717941
20.

Data-Adaptive Bias-Reduced Doubly Robust Estimation.

Vermeulen K, Vansteelandt S.

Int J Biostat. 2016 May 1;12(1):253-82. doi: 10.1515/ijb-2015-0029.

PMID:
27227724

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